Blood vessel segmentation and extraction using H-minima method based on image processing techniques
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Blood vessel segmentation and extraction using H-minima method based on image processing techniques Salma M. Boubakar Khalifa Albargathe 1 & Ersin Kamberli 2 & Fatma Kandemirli 2 & Javad Rahebi 3 Received: 9 September 2019 / Revised: 24 July 2020 / Accepted: 18 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
In this paper, the H-minima transform is used for blood vessel segmentation. The aim of this study is to get the high accuracy of blood vessel segmentation in retinal images. In this study the good result and good performance were got. We compared our result with other methods. Also for simulation result we implemented on DRIVE and STARE database. The proposed method shows very remarkable performance on pathological retinal images. For the implementing of the proposed method MATLAB 2019a software is used. The running time of this method was 1 s for each image and the average accuracy for STARE dataset and DRIVE dataset achieved to 0.9591 and 0.9672 respectively. Keywords Fundus camera . Image processing . Retina image
* Ersin Kamberli [email protected] Salma M. Boubakar Khalifa Albargathe [email protected] Fatma Kandemirli [email protected] Javad Rahebi [email protected]
1
Department of Material Science and Engineering, Kastamonu University, Kastamonu, Turkey
2
Department of Biomedical Engineering, Kastamonu University, Kastamonu, Turkey
3
Department of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey
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1 Introduction In addition to technological development, significant progress and a number of advances have been recorded in the computer techniques used in medical applications. Automatic image processing and analysis are widely used in the area of medical diagnosis and treatment. Recent developments in the field of medical image processing in particular enable the automatic detection of various characteristics, changes, diseases and degenerative problems via retinal images. Retinal image analyses use image processing techniques and is aimed at determining and monitoring diseases that can be detected via changes in the structure of the retina. Nguyen et al. [15] proposed a blood vessel segmentation method that uses multiscale line detection. The proposed method is based on changing the length of the line detector and as a result of this process various line detector with different measurements are gathered. To preserve the efficiency of this method while eliminating the cons of every individual line detector, the final segmentation of every retinal image is produced by varying measurements that are linearly joint. The performance this method was evaluated on 3 databases that avowedly available, these databases are DRIVE, STARE and REVIEW. The proposed method achieves high accuracy on DRIVE and STARE databases compared to other existing methods. The accuracy for DRIVE is 0.9407 and for STARE it is 0.9324.These result was gathered where the density of vessels is high and har
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